AysajanE/staged-workflow-runner

A manifest-driven runner for orchestrating high-stakes, staged OpenAI Responses workflows. It executes explicit task packs, preserves durable local evidence and reviewed handoffs between stages, and can run optionally through an additive supervisor lane that manages session state, scaffold staging, review automation, and final implementation bundles. Intended users: operators, supervisors, and developers running or auditing safety-critical LLM workflows. Key features: a core engine (automation/responses_runner_v2), CLIs for running workflows, creating review bundles, and supervisor flows; task-pack and example proof packs; built-in durable workspace contract; supervisor review loop (Codex and Claude reviewers, deterministic consolidation, operator acceptance); dry-run and static scaffold gates; local test suite and synthetic proof pack. Requirements: Python 3.10+, OPENAI_API_KEY (or .env); optional jsonschema, Codex/Claude CLIs for supervised reviews. Model defaults target GPT-5.5-family configurations.

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